‘Mapping Migrancies’: A discursive mapping approach to analyse lived experiences of skilled migration infrastructures
Bibliographic record
Abstract
The recent ‘infrastructural turn’ in migration studies has provided valuable insights into the emergence and functions of different aspects of migration infrastructure such as the commercial migration industry, social networks, and technological innovations (Xiang and Lindquist 2014). The focus of current scholarship, however, has been on how these infrastructures mobilise migrants, predominantly across irregular migration pathways. There remains a gap in exploring infrastructures of formal migration, and their entanglements with migrants’ own subjectivities. This paper reports on a research project that explores this gap by arguing for a new research agenda on migration infrastructure. The study uses a ‘discursive mapping’ approach involving in-depth interviews and mind-maps sketched by 27 research participants based in Australia and Canada as they narrated their migration experience. This paper draws upon the experiences of three migrants to illuminate how their journeys are intertwined with and shaped by migration infrastructures - particularly media and regulatory processes. By (re)centring the infrastructural focus on migrants’ own agencies, desires, and life-courses, this study presents nuanced understandings of the lived experience of skilled migration infrastructures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".